RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration

Zheng Xipeng; Yang Shunsheng; Xiang Wenchuan; Chen Yu

doi:doi:10.11648/j.ajbes.20180402.13

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RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration

Zheng Xipeng, Yang Shunsheng, Xiang Wenchuan, Chen Yu

Published in American Journal of Biological and Environmental Statistics (Volume 4, Issue 2)

Received: 26 June 2018 Accepted: 16 July 2018 Published: 9 August 2018

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Abstract

The arrangement of the sensors in the air pollutant distribution space was designed by segmented array. A data prediction model for RBF neural network was created. Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors in order to reduce the sensor arrangement cost. According to the measured values and the predicted data, Gaussian plume diffusion model for air pollution was created, and the quadratic optimization model and inversion method for inverse calculation of single pollution source and multi pollution source were built. Single pollution source and double pollution source was inversely optimized by three different intelligent optimized algorithms in experimental simulation in order to obtain the accurate information on pollution sources. The validity of this method was verified so as to provide a reference for subsequent research.

Published in	American Journal of Biological and Environmental Statistics (Volume 4, Issue 2)
DOI	10.11648/j.ajbes.20180402.13
Page(s)	66-73
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Air Pollution, Sensor, Gaussian Plume Diffusion Model, Intelligent Optimized Algorithm

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APA Style

Zheng Xipeng, Yang Shunsheng, Xiang Wenchuan, Chen Yu. (2018). RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration. American Journal of Biological and Environmental Statistics, 4(2), 66-73. https://doi.org/10.11648/j.ajbes.20180402.13

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ACS Style

Zheng Xipeng; Yang Shunsheng; Xiang Wenchuan; Chen Yu. RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration. Am. J. Biol. Environ. Stat. 2018, 4(2), 66-73. doi: 10.11648/j.ajbes.20180402.13

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AMA Style

Zheng Xipeng, Yang Shunsheng, Xiang Wenchuan, Chen Yu. RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration. Am J Biol Environ Stat. 2018;4(2):66-73. doi: 10.11648/j.ajbes.20180402.13

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@article{10.11648/j.ajbes.20180402.13,
  author = {Zheng Xipeng and Yang Shunsheng and Xiang Wenchuan and Chen Yu},
  title = {RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration},
  journal = {American Journal of Biological and Environmental Statistics},
  volume = {4},
  number = {2},
  pages = {66-73},
  doi = {10.11648/j.ajbes.20180402.13},
  url = {https://doi.org/10.11648/j.ajbes.20180402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbes.20180402.13},
  abstract = {The arrangement of the sensors in the air pollutant distribution space was designed by segmented array. A data prediction model for RBF neural network was created. Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors in order to reduce the sensor arrangement cost. According to the measured values and the predicted data, Gaussian plume diffusion model for air pollution was created, and the quadratic optimization model and inversion method for inverse calculation of single pollution source and multi pollution source were built. Single pollution source and double pollution source was inversely optimized by three different intelligent optimized algorithms in experimental simulation in order to obtain the accurate information on pollution sources. The validity of this method was verified so as to provide a reference for subsequent research.},
 year = {2018}
}

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TY  - JOUR
T1  - RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration
AU  - Zheng Xipeng
AU  - Yang Shunsheng
AU  - Xiang Wenchuan
AU  - Chen Yu
Y1  - 2018/08/09
PY  - 2018
N1  - https://doi.org/10.11648/j.ajbes.20180402.13
DO  - 10.11648/j.ajbes.20180402.13
T2  - American Journal of Biological and Environmental Statistics
JF  - American Journal of Biological and Environmental Statistics
JO  - American Journal of Biological and Environmental Statistics
SP  - 66
EP  - 73
PB  - Science Publishing Group
SN  - 2471-979X
UR  - https://doi.org/10.11648/j.ajbes.20180402.13
AB  - The arrangement of the sensors in the air pollutant distribution space was designed by segmented array. A data prediction model for RBF neural network was created. Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors in order to reduce the sensor arrangement cost. According to the measured values and the predicted data, Gaussian plume diffusion model for air pollution was created, and the quadratic optimization model and inversion method for inverse calculation of single pollution source and multi pollution source were built. Single pollution source and double pollution source was inversely optimized by three different intelligent optimized algorithms in experimental simulation in order to obtain the accurate information on pollution sources. The validity of this method was verified so as to provide a reference for subsequent research.
VL  - 4
IS  - 2
ER  -

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Author Information

Zheng Xipeng

School of Civil Engineering, Southwest Jiaotong University, Chengdu, China
Yang Shunsheng

School of Civil Engineering, Southwest Jiaotong University, Chengdu, China
Xiang Wenchuan

School of Civil Engineering, Southwest Jiaotong University, Chengdu, China
Chen Yu

Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China

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Plain Text BibTeX RIS

APA Style

Zheng Xipeng, Yang Shunsheng, Xiang Wenchuan, Chen Yu. (2018). RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration. American Journal of Biological and Environmental Statistics, 4(2), 66-73. https://doi.org/10.11648/j.ajbes.20180402.13

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ACS Style

Zheng Xipeng; Yang Shunsheng; Xiang Wenchuan; Chen Yu. RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration. Am. J. Biol. Environ. Stat. 2018, 4(2), 66-73. doi: 10.11648/j.ajbes.20180402.13

Copy | Download

AMA Style

Zheng Xipeng, Yang Shunsheng, Xiang Wenchuan, Chen Yu. RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration. Am J Biol Environ Stat. 2018;4(2):66-73. doi: 10.11648/j.ajbes.20180402.13

Copy | Download

@article{10.11648/j.ajbes.20180402.13,
  author = {Zheng Xipeng and Yang Shunsheng and Xiang Wenchuan and Chen Yu},
  title = {RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration},
  journal = {American Journal of Biological and Environmental Statistics},
  volume = {4},
  number = {2},
  pages = {66-73},
  doi = {10.11648/j.ajbes.20180402.13},
  url = {https://doi.org/10.11648/j.ajbes.20180402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbes.20180402.13},
  abstract = {The arrangement of the sensors in the air pollutant distribution space was designed by segmented array. A data prediction model for RBF neural network was created. Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors in order to reduce the sensor arrangement cost. According to the measured values and the predicted data, Gaussian plume diffusion model for air pollution was created, and the quadratic optimization model and inversion method for inverse calculation of single pollution source and multi pollution source were built. Single pollution source and double pollution source was inversely optimized by three different intelligent optimized algorithms in experimental simulation in order to obtain the accurate information on pollution sources. The validity of this method was verified so as to provide a reference for subsequent research.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration
AU  - Zheng Xipeng
AU  - Yang Shunsheng
AU  - Xiang Wenchuan
AU  - Chen Yu
Y1  - 2018/08/09
PY  - 2018
N1  - https://doi.org/10.11648/j.ajbes.20180402.13
DO  - 10.11648/j.ajbes.20180402.13
T2  - American Journal of Biological and Environmental Statistics
JF  - American Journal of Biological and Environmental Statistics
JO  - American Journal of Biological and Environmental Statistics
SP  - 66
EP  - 73
PB  - Science Publishing Group
SN  - 2471-979X
UR  - https://doi.org/10.11648/j.ajbes.20180402.13
AB  - The arrangement of the sensors in the air pollutant distribution space was designed by segmented array. A data prediction model for RBF neural network was created. Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors in order to reduce the sensor arrangement cost. According to the measured values and the predicted data, Gaussian plume diffusion model for air pollution was created, and the quadratic optimization model and inversion method for inverse calculation of single pollution source and multi pollution source were built. Single pollution source and double pollution source was inversely optimized by three different intelligent optimized algorithms in experimental simulation in order to obtain the accurate information on pollution sources. The validity of this method was verified so as to provide a reference for subsequent research.
VL  - 4
IS  - 2
ER  -

Copy | Download